In [47]:
%matplotlib inline
import skimage
print(skimage.__version__)
import numpy as np
from PIL import Image, ImageFont, ImageDraw
from PIL.ImageChops import add, subtract, multiply, difference, screen
import PIL.ImageStat as stat
from skimage.io import imread, imsave, imshow, show, imread_collection, imshow_collection
from skimage import color, viewer, exposure, img_as_float, data
from skimage.transform import SimilarityTransform, warp, swirl
from skimage.util import invert, random_noise, montage
import matplotlib.image as mpimg
import matplotlib.pylab as plt
from IPython.display import display
from scipy.ndimage import affine_transform, zoom
from scipy import misc
Operaciones básicas de lectura y escritura¶
Reading, saving, and displaying an image using PIL¶
(usaremos ésta librerÃa para hacer varias operaciones sobre imágenes)
In [116]:
im = Image.open("../images/parrot.png") # read the image, provide the correct path
print(im.width, im.height, im.mode, im.format, type(im))
# 453 340 RGB PNG <class 'PIL.PngImagePlugin.PngImageFile'>
#im.show() # display the image (in window)
display(im)
In [51]:
im_g = im.convert('L') # convert the RGB color image to a grayscale image
im_g.save('../images/parrot_gray.png') # save the image to disk
display(Image.open("../images/parrot_gray.png")) # read the grayscale image from disk and show
Reading, saving, and displaying an image using Matplotlib¶
In [117]:
im = mpimg.imread("../images/hill.png") # read the image from disk as a numpy ndarray
print(im.shape, im.dtype, type(im)) # this image contains an α channel, hence num_channels= 4
# (960, 1280, 4) float32 <class 'numpy.ndarray'>
plt.figure(figsize=(10,10))
plt.imshow(im) # display the image
plt.axis('off')
plt.show()
In [118]:
type(im)
Out[118]:
In [8]:
im1 = im
im1[im1 < 0.5] = 0 # make the image look darker
plt.imshow(im1)
plt.axis('off')
plt.tight_layout()
plt.savefig("../images/hill_dark.png") # save the dark image
plt.close()
im = mpimg.imread("../images/hill_dark.png") # read the dark image
plt.figure(figsize=(10,10))
plt.imshow(im)
plt.axis('off') # no axis ticks
plt.tight_layout()
plt.show()
Reading, saving, and displaying an image using scikit-image¶
In [53]:
im = imread("../images/parrot.png") # read image from disk, provide the correct path
print(im.shape, im.dtype, type(im))
plt.axis('off'), imshow(im), show()
Out[53]:
In [55]:
im_gray = color.rgb2gray(im)
imsave('../images/parrot_gray_skimage.png', im_gray) # save image to disk
im = imread("../images/parrot_gray_skimage.png")
plt.axis('off'), imshow(im), show()
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Espacios de color¶
RGB¶
In [68]:
import IPython.display as ipd
ipd.Image("../images/RGB_Cube_Show_lowgamma_cutout_b.png", width=500)
Out[68]:
In [119]:
im = Image.open("../images/parrot.png")
ch_r, ch_g, ch_b = im.split() # split the RGB image into 3 channels: R, G and B
# we shall use matplotlib to display the channels
plt.figure(figsize=(18,6))
plt.subplot(1,3,1); plt.imshow(ch_r, cmap=plt.cm.Reds); plt.axis('off')
plt.subplot(1,3,2); plt.imshow(ch_g, cmap=plt.cm.Greens); plt.axis('off')
plt.subplot(1,3,3); plt.imshow(ch_b, cmap=plt.cm.Blues); plt.axis('off')
plt.tight_layout()
plt.show() # show the R, G, B channels
Plotting the histograms of pixel values for the RGB channels of an image¶
In [71]:
pl = im.histogram()
plt.bar(range(256), pl[:256], color='r')
plt.bar(range(256), pl[256:2*256], color='g')
plt.bar(range(256), pl[2*256:], color='b')
plt.show()
In [70]:
pl = im.histogram()
plt.bar(range(256), pl[:256], color='r', alpha=0.5)
plt.bar(range(256), pl[256:2*256], color='g', alpha=0.4)
plt.bar(range(256), pl[2*256:], color='b', alpha=0.3)
plt.show()
HSV¶
In [74]:
ipd.Image("../images/HSV_color_solid_cylinder_saturation_gray.png", width=500)
Out[74]:
In [75]:
ipd.Image("../images/HSV_color_solid_cone_chroma_gray.png", width=500)
Out[75]:
HUE: Hue is the color portion of the model, expressed as a number from 0 to 360 degrees:
- Red falls between 0 and 60 degrees.
- Yellow falls between 61 and 120 degrees.
- Green falls between 121 and 180 degrees.
- Cyan falls between 181 and 240 degrees.
- Blue falls between 241 and 300 degrees.
- Magenta falls between 301 and 360 degrees.
SATURATION: Saturation describes the amount of gray in a particular color, from 0 to 100 percent. Reducing this component toward zero introduces more gray and produces a faded effect. Sometimes, saturation appears as a range from 0 to 1, where 0 is gray, and 1 is a primary color.
VALUE (OR BRIGHTNESS): Value works in conjunction with saturation and describes the brightness or intensity of the color, from 0 to 100 percent, where 0 is completely black, and 100 is the brightest and reveals the most color.
CHROMA: The colorfulness relative to the brightness of a similarly illuminated white. Equal to the range of the RGB values (saturation is equal to the chroma over the maximum chroma in that slice of the cone).
In [76]:
im = imread("../images/parrot.png")
plt.imshow(im)
im.shape
Out[76]:
In [77]:
im_hsv = color.rgb2hsv(im)
im_hsv.shape
Out[77]:
In [81]:
#plt.gray()
plt.figure(figsize=(18,6))
plt.subplot(131), plt.imshow(im_hsv[...,0]), plt.title('h', size=20),
plt.axis('off')
plt.subplot(132), plt.imshow(im_hsv[...,1]), plt.title('s', size=20),
plt.axis('off')
plt.subplot(133), plt.imshow(im_hsv[...,2]), plt.title('v', size=20),
plt.axis('off')
plt.show()
In [127]:
im = imread("../images/parrot.png") # read image from disk, provide the correct path
print(im.shape, im.dtype, type(im))
hsv = color.rgb2hsv(im) # from RGB to HSV color space
hsv[:, :, 1] = 0.5 # change the saturation
im1 = color.hsv2rgb(hsv) # from HSV back to RGB
imsave('../images/parrot_hsv.png', im1) # save image to disk
im = imread("../images/parrot_hsv.png")
plt.axis('off'), imshow(im), show()
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Converting image data structures¶
In [120]:
im0 = Image.open('../images/flowers.png') # read image into an Image object with PIL
print(im0.mode, im0.format)
In [121]:
im = np.array(im0) # create a numpy ndarray from the Image object
imshow(im) # use skimage imshow to display the image
plt.axis('off'), show()
im.shape
Out[121]:
In [125]:
im[...,3]=200
imshow(im) # use skimage imshow to display the image
plt.axis('off'), show()
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Computing the basic statistics of an image¶
In [130]:
im = Image.open('../images/parrot.png')
In [131]:
s = stat.Stat(im)
print(s.extrema) # maximum and minimum pixel values for each channel R, G, B
print(s.count)
print(s.mean)
print(s.median)
print(s.stddev)
Combining multiple channels of an image¶
In [133]:
im = Image.merge('RGB', (ch_b, ch_g, ch_r)) # swap the red and blue channels obtained last time with split()
display(im)
α-blending two images¶
In [137]:
im1 = Image.open("../images/parrot.png")
im2 = Image.open("../images/hill.png")
im1 = im1.convert('RGBA') # two images have different modes, must be converted to the same mode
im2 = im2.resize((im1.width, im1.height), Image.BILINEAR) # two images have different sizes, must be converted to the same size
im = display(Image.blend(im1, im2, alpha=0.5))
Superimposing two images¶
In [110]:
im1 = Image.open("../images/parrot.png")
im2 = Image.open("../images/hill.png").convert('RGB').resize((im1.width, im1.height))
display(multiply(im1, im2))
Adding two images¶
In [111]:
display(add(im1, im2))
Computing the difference between two images¶
In [112]:
from PIL.ImageChops import subtract, multiply, screen, difference, add
im1 = Image.open("../images/goal1.png") # load two consecutive frame images from the video
im2 = Image.open("../images/goal2.png")
im = difference(im1, im2)
plt.figure(figsize=(20,25))
im.save("../images/goal_diff.png")
plt.subplot(311)
plt.imshow(im1)
plt.axis('off')
plt.subplot(312)
plt.imshow(im2)
plt.axis('off')
plt.subplot(313)
plt.imshow(im), plt.axis('off')
plt.show()
